
Object detection plays a critical role in many real-world applications, such as autonomous driving and video surveillance. While deep learning has achieved tremendous success in this task [1]}, [2]}, [3]}, [4]}, [5]}, the speed-accuracy trade-off of the resulting models remains a challenge. This is particularly important for real-time prediction on embedded platforms, whose limited memory and computation power impose strict constraints on the deep network architecture.
